The growing demand of communication systems, in particular of the hands-free type has led to an increased effort in developing acoustic echo cancellers. Such acoustic echo cancellers require efficient filtering techniques with low computational burden and delay.
As is commonly known, the use of, e.g., a handset in a vehicle during driving significantly reduces the attention of the driver and increases the risk of accidents. Here, hands-free equipment allows the driver to concentrate more on the traffic and increases security. One reason that hands-free equipment is not widely used is due to the poor quality of available systems. Another reason is that available hands-free equipment usually work on a switching basis thus requiring a high talking discipline by both users as only half duplex communication is possible.
An approach towards full duplex communication can be achieved by acoustic echo cancellation where the echo is not suppressed but compensated, as described, e.g., in “On the Implementation of a Partitioned Block Frequency Domain Adaptive Filter (PBFDAF) for Long Acoustic Echo Cancellation”, José M. P. Borrallo, Mariano G. Otero, Signal Processing 27 (1992), pp. 309-315.
FIG. 1 shows the related fundamentals of echo cancellation. In particular, FIG. 1 shows a loudspeaker 200 and a microphone 202 of, e.g., a hands-free communication device or a teleconference system. Further, an echo propagation path 206 is shown in dotted lines between the loudspeaker 200 and the microphone 202. Usually, a near end speaker of the communication device receives acoustic information via the loudspeaker 200 and transmits information to a far end speaker via the microphone 202. However, it is through the feedback propagation of sound waves outputted by the loudspeaker 200 that echoes are back propagated to the far end speaker.
As shown in FIG. 1, to overcome this drawback an adaptive filter 208 may be used to generate a synthetic echo for antiphase compensation with the real echo. In other words, the adaptive filter 208 is a model of the echo path 206 and has to adapt to changing real world environments due to, e.g., movement of the near end speaker or changing of the environment where the hands-free communication device is installed.
As also shown in FIG. 1, to carry out the antiphase w compensation, there is provided a summation point 210 where the real echo and the synthetic echo are subtracted. However, as the adaptive filter 208 will usually not achieve a complete modelling of the real world environment after summation, there will remain an error signal that may then be back propagated to the far end speaker.
The adaptive filter 208 may be implemented as a time domain or a frequency domain adaptive filter. Further, the filter has to be adaptive to adjust to different room environments and to the movements of the near end talker. The process of adjusting the filter coefficients is called convergence and the speed of convergence defines to a large extent the performance of the acoustic echo canceller.
The adjusting of filter coefficients relies on the input signal to the adaptive filter, an estimation of the power of this input signal and finally on the error signal between the input signal filtered in the adaptive filter and the signal received through the microphone 202, i.e. the pathy 206 modeled through the adaptive filter.
FIG. 2 shows a signal flowgraph for the estimation of the power of the input signal. Here, the instantaneous power of the input signal is derived and then weighted with a factor β in a first multiplier 214. To also execute a corrector step the estimated power level is rated by a factor (1-β) through a second multiplier 216, delayed in a delay unit 218 and finally added to the instantaneous power through an adder 220, as shown in FIG. 2.
However, the approach to power level estimation shown in FIG. 2 does not take into account any information on the input signal but uses only predefined factors irrespective of the signal characteristics. Still further, any prevailing surrounding conditions have no impact on the estimation of the power level, e.g., such as surrounding noise. Therefore, the usual approach illustrated in FIG. 2 achieves only a limited convergence behaviour of the adaptive filter with the related negative impact on the communication quality.